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Leveraging Document AI for Faster and Insightful Decision-Making

April 14, 2022 - Team EdgeVerve

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Document AI uses new-age technology like Artificial Intelligence, Machine Learning, and Natural Language Processing to train software to imitate human actions when reviewing documents. Unlike humans, AI-enabled solutions like XtractEdge are incapable of omitting granular data; hence, data extracted are relevant, purposeful, and without any human error or bias.

What is Document AI – From Customers’ Perspective

Customers prefer Intelligent Document Processing with minimal to negligible human touch in the automation process. The former desire meaningful real-time data to aid in faster and more insightful decision-making, available as and when needed.

Hence, their expectations revolve around high extraction accuracy, with models that facilitate document discovery from unstructured/semi-structured documents in multiple templates, file, and image formats.

Also, mere extraction of data doesn’t serve the purpose of customers. When customers consider Document AI , they expect the solution to seamlessly fit into their business landscape. That’s how Document AI can foster significant improvements in day-to-day operations.

Expected Features of Document AI

The main focus of how customers perceive Document AI should be when deploying a solution. Here is a list of expected features derived from customers:

Obviously, some of the above features will be revealed gradually post successful initial deployment of the Intelligent Document Processing solution. But, it is a given fact that customers’ expectations with Document AI have evolved with time. They look at Document AI not as a mere content extraction platform; instead, they consider it a one-stop solution capable of handling touchless end-to-end flow that hosts the features mentioned above and many others.

Challenges in Document Extraction

Enterprises handle a vast pool of data daily. And 90% of data is unstructured and locked in documents of various formats, which makes extracting the desired information cumbersome at any given time. Adding to this is the inability of legacy systems of data extraction to provide output data in a consumable format by downstream applications.

When discussing customer expectations, we hardly refer to solutions that breaks/dissects various documents and extracts content only. Instead, we indicate a platform capable of identifying and sharing ‘meaningful’ content in context for their operations.

Following are a few challenges faced by legacy approaches to data extraction:

Document complexity: As per experts, nearly 70% of organizations still depend on old, paper-based documents. Scanning each document lost in different folders and various email trails is both time and labor-intensive, also prone to mindless human errors.

Domain specificity: Every organization uses specific document types. For instance, a few companies prefer Google Doc Suite, while others use Microsoft Suite. Then, there are types and formats to maintain waybills, loan applications, tax forms, invoices, and so on. Hence, any document extraction tool should be able to identify such domain-specific context.

Bulk data: Enterprises deal with bulk data, which is not humanly possible to manage, maintain, and extract insights. Also, not all solutions can handle such volumes of data, which impedes the speed of data extraction, eventually nullifying the purpose of utilizing technology.

Disjointed approach: Many solutions are disjointed; hence, major enterprise document problems remain unsolved.

Conclusion

Based on customer expectations, large-scale Document AI implementation handled various business needs across the industry, including insurance, pharma, financial, or utilities.

The sole benefit of Document AI is delivering business benefits that align with customers’ organizational objectives. Of course, the points mentioned above are just the tip of the iceberg. There are scopes of improvements when they bring automation to document processing in their landscape. And, as customers gradually delve deeper into the discovery phase, they realize what all is capable of automation using Document AI extraction and processing platform like XtractEdge.

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